Mobilizing your AI ambitions: Three questions for Life Science organizations to get started

Authored by: Todd Quartiere, Prathima Guniganti & Jen Burke

Integrating Artificial Intelligence (AI) across health industry operations was seen as an optional, leading edge only a couple of years ago. Predictive analytics often dominated headlines, and automation was a key effort with centralized programs. AI has since become a must-have, pressing requirement for technologies across organizations looking to modernize and mobilize their ambitious goals. In this thought series we explore what it takes for organizations to achieve those goals, starting with a clear vision & strategy, then piloting applications for specific use cases, and finally to scaling valuable solutions.

Getting started can be a challenge for small and midsize pharmaceutical companies with limited resources. The AI landscape is ever-changing, with expert knowledge on how to future-proof your organization in short supply. Taking the first step often means mobilizing a cross-functional and multidisciplinary team to coalesce a clear vision and robust strategy. By combining business knowledge with external market intelligence & expertise, companies can work through these three key questions that set the foundation for AI programs to achieve the adoption and return on investment (ROI) they hope to realize.

1. What outcomes do you hope to improve by applying AI?

Before you evaluate where AI can be applied, consider what you are hoping to achieve. It is important to understand, articulate, and plan for an AI strategy to solve existing business needs. Having clear goals and direction will enable you to identify the prerequisites (like a data strategy) to integrating AI into broader strategic objectives and evaluating the impact, effort, and feasibility of these applications.

That broad range of applications often spans nearly every division of an organization. Often applications can be envisioned on a spectrum of current business process efficiencies, to moonshot disruptions of business operations, with a comprehensive AI journey inclusive of both. Being able to achieve quick wins for straightforward applications can generate momentum for longer timelines. Often these quick wins focus on:

• Intelligent automation for administrative tasks
• Predictive analytics for sales forecasting
• Computer vision and natural language processing for document processing

Equally important is creating timelines for more disruptive opportunities like AI Agents. As pharma organizations look to stay competitive and cost-efficient, there has been an array of applications brought forward to consider as a part of the journey, including:

• Generative AI (GenAI) for early-stage drug discovery
• Analyzing population and patient data for clinical trial diversification
• Developing digital twins for patient journey modeling
• Supporting regulatory submissions with intelligent automation of compliance checking or document creation
• Language translation or reporting for global, multinational product launches

2. How will you quantify the value of applying AI?

With key stakeholders across the organization and high investment requirements, AI initiatives often require strong, iterative business cases that secure staged buy-in with appropriate resourcing. Each stage of investment carries the organization forward through a journey of setting the vision & strategy, piloting & learning about applications, and finally scaling & realizing value. That journey is challenging when novel evolutions for a specific model or application require the strategy to change course and futureproofing both the program and the AI applications can make the difference between positive or negative returns on the investment.

Theorized returns on time efficiencies often must be translated into underlying key financial performance indicators, like staffing volumes or operating revenue. When evaluating those factors, organizations must consider:

• Cost Reductions – What expenses between your workforce, technical architecture, and business processes will be reduced to offset new program or technical expenses?
• Increased revenue – How much more could a product earn if the drug discovery, preclinical, clinical, or commercialization phases were reduced?
• Workforce Allocation – Who needs to be upskilled and incorporated into AI-enabled workflows, and how will the additional time savings translate into operating margins?
• Upfront & Continuous Financial Investment– How quickly does the organization want to reach each stage of its AI journey, what is the organizational capacity to reach that goal, and what is the opportunity cost of not reaching that goal?
• In-house Knowledge & External Expertise – Who will be able to support the program internally, and in what capacities will they need to be extended by external expertise?
• Time to Value Realization – What will the appropriate balance of smaller, faster returns to slower, larger returns need to be across the portfolio?

3. How will you balance building momentum with realizing value?

Though organizations often try and skip ahead, integrating AI is the last step in a longer digital transformation. There are steps that should be taken to prepare the data, foster organizational readiness, and evaluate the applications across the organization. A comprehensive, sustainable roadmap helps balance quick wins with long-term initiatives. Organizations at this stage should consider:

• Starting with low-hanging fruit – Identify where current workflows can be augmented with AI for immediate efficiencies without significant infrastructure changes or training.
• Building on existing strengths – Leverage your company’s agility and specific expertise to find unique AI applications that provide a competitive edge.
• Placing a few big bets – Prioritize one or two larger investments that transform business operations or create channels for new revenue.
• Collaborations and partnerships – AI journeys are complex and challenging, with startups, nonprofits, and academics often partnering to create pre-competitive or foundational technologies, insights, and collaboratives that can give organizations a lead over their competitors while cutting initial costs.
• Iterations and scale – Start with pilot projects, learn from the results, and gradually scale valuable initiatives across the organization.

The role of Vynamic in your AI journey

At Vynamic, we understand that turning an AI strategy into reality can feel daunting—especially at the start. The process involves much more than implementing new technologies; it requires transforming how your organization works at its core. That is why we recommend starting small, building momentum with early successes, and scaling thoughtfully over time.

Our AI Mobilization offering is designed to help you take those first steps, guiding you from defining a clear vision to scaling AI initiatives that deliver real, measurable value. We know the health industry and understand its unique challenges. Our team will work alongside you to identify high-impact opportunities, pilot innovative solutions, and ensure AI becomes a driver of meaningful, long-term success for your organization.

The AI transformation in Life Sciences is still in its early stages. By acting now, even with small, manageable steps, you will be positioning your organization to realize value faster, stay competitive, and ultimately improve patient outcomes. Together, we can help make your AI vision not only achievable but also aligned with your broader business goals. Let’s start this journey one step at a time and build toward a future that is both innovative and impactful.

Sources:

1. https://hbr.org/2024/02/genai-can-help-small-companies-level-the-playing-field
2. https://www.ey.com/en_us/insights/life-sciences/how-pharma-can-benefit-from-using-genai-in-drug-discovery